Impact of the COVID-19 pandemic, its related social restriction measures and vaccination campaign on emergency department attendances for epileptic seizures: An interrupted time-series analysis.

AIM: To investigate the impact of the outbreak of the COVID-19 pandemic, its related social restriction measure (national lockdown) and vaccination campaign on emergency department (ED) accesses for epileptic seizures. METHODS: Retrospective observational analysis conducted on a consecutive cohort of patients who sought medical care at the ED of the General Hospital of Merano, Italy, from January 1, 2015, to December 31, 2021. We investigated the monthly ED attendances for epileptic seizures between the periods before and after the outbreak of the COVID-19 pandemic and the national lockdown (March 2020) using an interrupted time-series analysis with data standardized for 1000 accesses/month. As a further temporal cutoff, we used the start of the national vaccination campaign. RESULTS: Between January 1, 2015, and December 31, 2021, a total of 415,005 ED attendances were recorded; 1,254 (0.3 %) were due to epileptic seizures. No significant difference was found in the rate of standardized ED accesses for epileptic seizures in March 2020 (time point of interest) to the pre-pandemic trend (0.33/1000; 95 %CI: -1.05 to 1.71; p = 0.637). Similarly, there was no difference between the pre- and post-pandemic trends (-0.02/1000; 95 %CI: -0.11 to 0.06; p = 0.600). When adopting January 2021 as time point of interest, we found no difference to the pre-vaccination trend (0.83/1000; 95 %CI: -0.48 to 2.15), and no difference in the pre- and post-vaccination trends (-0.12/1000; 95 %CI: -0.27 to 0.04). CONCLUSIONS: The COVID-19 pandemic and its related social restrictions (lockdown), as well as the COVID-19 national vaccination campaign, had little impact on ED accesses for epileptic seizures.

Progression to refractory status epilepticus: A machine learning analysis by means of classification and regression tree analysis.

BACKGROUND AND OBJECTIVES: to identify predictors of progression to refractory status epilepticus (RSE) using a machine learning technique. METHODS: Consecutive patients aged ≥ 14 years with SE registered in a 9-years period at Modena Academic Hospital were included in the analysis. We evaluated the risk of progression to RSE using logistic regression and a machine learning analysis by means of classification and regression tree analysis (CART) to develop a predictive model of progression to RSE. RESULTS: 705 patients with SE were included in the study; of those, 33 % (233/705) evolved to RSE. The progression to RSE was an independent risk factor for 30-day mortality, with an OR adjusted for previously identified possible univariate confounders of 4.086 (CI 95 % 2.390-6.985; p < 0.001). According to CART the most important variable predicting evolution to RSE was the impaired consciousness before treatment, followed by acute symptomatic hypoxic etiology and periodic EEG patterns. The decision tree identified 14 nodes with a risk of evolution to RSE ranging from 1.5 % to 90.8 %. The overall percentage of success in classifying patients of the decision tree was 79.4 %; the percentage of accurate prediction was high, 94.1 %, for those patients not progressing to RSE and moderate, 49.8 %, for patients evolving to RSE. CONCLUSIONS: Decision-tree analysis provided a meaningful risk stratification based on few variables that are easily obtained at SE first evaluation: consciousness before treatment, etiology, and severe EEG patterns. CART models must be viewed as potential new method for the stratification RSE at single subject level deserving further exploration and validation.

Prognostic role of albumin, lactate-to-albumin ratio and C-reactive protein-to-albumin ratio in infected patients.

INTRODUCTION: The prognostic evaluation of the septic patient has recently been enriched by some predictive indices such as albumin concentration, lactate/albumin ratio (LAR) and C-reactive protein/albumin ratio (CAR). The performance of these indices has been evaluated in septic patients in intensive care, but until now their performance in infected patients in the Emergency Department (ED) has not been evaluated. AIM: To investigate the potential prognostic role of albumin, LAR and CAR in patients with infection in the ED. METHODS: Single-centre prospective study performed between 1 January 2021 and 31 December 2021 at the ED of the Merano Hospital (Italy). All patients with infection were enrolled. The study outcome was death within 30 days. The predictive ability of albumin, LAR and CAR was assessed by area under the receiver operating characteristic curves (AUROCs). A multivariate logistic regression model was used to examine the association of the indices with 30-day mortality, with comorbidity, acute urgency and severity of infection as covariates. RESULTS: The study enrolled 962 patients with an infectious status. The overall 30-day mortality rate was 8.9% (86/962). The AUROC of albumin was 0.831 (95% CI 0.795-868), while for LAR this was 0.773 (CI95% 0.719-0.827) and for CAR 0.718 (CI95% 0.664-0.771). The odds ratio for 30-day mortality for albumin was 3.362 (95% CI 1.904-5.936), for ln(LAR) 2.651 (95% CI 1.646-4.270) and for ln(CAR) 1.739 (95% CI 1.326-2.281). CONCLUSIONS: All three indices had a good discriminatory ability for the risk of short-term death in patients with infection, indicating their promising use in the ED as well as in the ICU. Further studies are needed to confirm the better performance of albumin compared to LAR and CAR.

Human intelligence versus Chat-GPT: who performs better in correctly classifying patients in triage?

INTRODUCTION: Chat-GPT is rapidly emerging as a promising and potentially revolutionary tool in medicine. One of its possible applications is the stratification of patients according to the severity of clinical conditions and prognosis during the triage evaluation in the emergency department (ED). METHODS: Using a randomly selected sample of 30 vignettes recreated from real clinical cases, we compared the concordance in risk stratification of ED patients between healthcare personnel and Chat-GPT. The concordance was assessed with Cohen's kappa, and the performance was evaluated with the area under the receiver operating characteristic curve (AUROC) curves. Among the outcomes, we considered mortality within 72 h, the need for hospitalization, and the presence of a severe or time-dependent condition. RESULTS: The concordance in triage code assignment between triage nurses and Chat-GPT was 0.278 (unweighted Cohen's kappa; 95% confidence intervals: 0.231-0.388). For all outcomes, the ROC values were higher for the triage nurses. The most relevant difference was found in 72-h mortality, where triage nurses showed an AUROC of 0.910 (0.757-1.000) compared to only 0.669 (0.153-1.000) for Chat-GPT. CONCLUSIONS: The current level of Chat-GPT reliability is insufficient to make it a valid substitute for the expertise of triage nurses in prioritizing ED patients. Further developments are required to enhance the safety and effectiveness of AI for risk stratification of ED patients.

Establishing a common ground: the future of triage systems.

Triage systems, crucial for Emergency Departments, face unresolved doubts and issues that have not been addressed, coupled with increasing fragmentation due to a growing body of new evidence. International collaboration is essential for evidence-based triage solutions.

Challenges and peculiarities of nursing activities in intermediate care units compared with internal medicine wards: A prospective study.

BACKGROUND: Intermediate Care Units (IMCs) are specialized facilities located within other departments in many Western countries. They are designed to manage patients with conditions that are not severe enough to require an intensive care unit. IMCs aim to fill the gap between regular wards and intensive care units, necessitating an adequate allocation of nursing resources. AIMS: The aims of the study are to (1) evaluate and compare the nursing workload for patients admitted to a regular ward or to an IMC; (2) quantify nursing workload in terms of activities and time spent to perform them; and (3) evaluate which patient characteristics predict nursing work overload. STUDY DESIGN AND METHODS: This is an observational, prospective, single-centre study. We included patients admitted to the Internal Medicine department in a general hospital in Italy, between 1 September and 31 December 2022, either in the regular ward or in the IMC. Clinical characteristics, comorbidity, functionality, frailty, severity and acuity of patients were recorded using validated assessment tools. Nursing activities in the first 3 days of hospitalization were recorded and standardized as activities/5 min/patient/day. An average number of nursing activities/5 min/patient/day exceeding the 85th percentile was considered nursing work overload. Multivariate logistic regression models were conducted to identify patient-related risk factors associated with nursing work overload. RESULTS: We included 333 patients, 55% (183/333) admitted to the IMC and 45% (150/333) to the regular ward. In the IMC, the average nursing activities were 32.4/5 min/patient/day compared with 22.6 in the regular ward. Nursing work overload was found in 6% (9/150) of patients admitted to the regular ward compared with 23% (42/183) in the IMC. CONCLUSION: There is a significantly higher demand for nursing care among patients in the IMC, with higher daily average of nursing activities. RELEVANCE TO CLINICAL PRACTICE: The allocation of nursing resources within the IMC should be greater than in the regular ward because of higher workload.

Comparison of the status epilepticus severity score and the epidemiology-based mortality score in predicting 30-day mortality and status epilepticus cessation.

OBJECTIVE: To evaluate the role of the Status Epilepticus Severity Score (STESS) and the Epidemiology-based Mortality score (EMSE) in predicting 30-day mortality and SE (Status epilepticus) cessation, and their prognostic performance in subgroups of patients with specific characteristics. METHODS: We reviewed consecutive episodes of SE occurring in patients aged ≥14 years at Baggiovara Civil Hospital (Modena, Italy) from 2013 to 2021. We evaluated the predictive accuracy of EMSE and STESS for 30-day mortality and SE cessation through stepwise regression binary logistic models adjusted for possible univariate clinical confounders. RESULTS: Seven hundred and eleven patients were enrolled. The mean value of STESS was 3.2 (SD 1.7) and of EMSE was 80.1 (SD 52.6). Within 30 days of the onset of SE, 28.4% of patients (202/711) died. EMSE had higher discriminatory ability for 30-day mortality compared with STESS (AUROC: 0.799; 95% CI: 0.765-0.832 versus 0.727; 95% CI: 0.686-0.766, respectively; p = 0.014). SE cessation within 1 h for convulsive SE and within 12 h for nonconvulsive SE was achieved in 35.3% (251/711) of patients. No significant difference was found between EMSE and STESS in discriminatory ability for SE cessation (AUROC: 0.516; 95% CI: 0.488-0.561 and 0.518; 95% CI: 0.473-0.563, respectively; p = 0.929). EMSE was superior to STESS in predicting 30-day mortality in patients with specific characteristics. No difference between the two scores was found in predicting SE cessation in subgroups of patients with specific characteristics. CONCLUSIONS: EMSE seems superior to STESS in predicting 30-day mortality, particularly in specific patient categories. Conversely, there is no difference in the ability of these scores in predicting SE cessation, which is overall rather low.

Retrospective External Validation of the Status Epilepticus Severity Score (STESS) to Predict In-hospital Mortality in Adults with Nonhypoxic Status Epilepticus: A Machine Learning Analysis.

BACKGROUND: The objective of this study was to validate the value of the Status Epilepticus Severity Score (STESS) in the prediction of the risk of in-hospital mortality in patients with nonhypoxic status epilepticus (SE) using a machine learning analysis. METHODS: We included consecutive patients with nonhypoxic SE (aged ≥ 16 years) admitted from 2013 to 2021 at the Modena Academic Hospital. A decision tree analysis was performed using in-hospital mortality as a dependent variable and the STESS predictors as input variables. We evaluated the accuracy of STESS in predicting in-hospital mortality using the area under the receiver operating characteristic curve (AUROC) with 95% confidence interval (CI). RESULTS: Among 629 patients with SE, the in-hospital mortality rate was 23.4% (147 of 629). The median STESS in the entire cohort was 2.9 (SD 1.6); it was lower in surviving compared with deceased patients (2.7, SD 1.5 versus 3.9, SD 1.6; p < 0.001). Of deceased patients, 82.3% (121 of 147) had scores of 3-6, whereas 17.7% (26 of 147) had scores of 0-2 (p < 0.001). STESS was accurate in predicting mortality, with an AUROC of 0.688 (95% CI 0.641-0.734) only slightly reduced after bootstrap resampling. The most significant predictor was the seizure type, followed by age and level of consciousness at SE onset. Nonconvulsive SE in coma and age ≥ 65 years predicted a higher risk of mortality, whereas generalized convulsive SE and age < 65 years were associated with a lower risk of death. The decision tree analysis using STESS variables correctly classified 90% of survivors and 34% of nonsurvivors after the SE, with an overall risk of error of 23.1%. CONCLUSIONS: This validation study using a machine learning system showed that STESS is a valuable prognostic tool. The score appears particularly accurate and effective in identifying patients who are alive at discharge (high negative predictive value), whereas it has a lower predictive value for in-hospital mortality.

Daily triage audit can improve nurses' triage stratification: A pre-post study.

AIMS: The objective was to evaluate whether the error rate in the application of the triage system decreased after the introduction of daily auditing, and it was also evaluated if the agreement rate between physician and nurse on triage priority levels increased after the introduction of daily auditing and if the error-related variables in the pre-intervention period changed in the post-intervention period. DESIGN: A quasi-experimental study was performed with a pre-post design, between June 2019 and June 2021 in one emergency department. METHODS: The accuracy and error rate of triage in the pre- and post-intervention period were compared. Univariate and multivariate logistic regression analyses were performed to explore the relationships between the variables related to the error. The comparison between the priority level assigned by the physician and the triage nurse was analysed using Cohen's K. RESULTS: Nine hundred four patients were enrolled in the pre-intervention period and 869 in the post-intervention period. The error rate in the pre-intervention period was 23.3% and in the post-intervention period was 9.7%. The concordance between the degree of priority expressed by the physician and the nurse varied from a quadratically weighted Cohen's K of 0.447 in the pre-intervention period to 0.881 in the post-intervention period. CONCLUSION: Daily auditing is a clinical procedure that improves the nurse's application of the triage system. Daily auditing has reduced errors by the nurse, improving performance and concordance with the physician. IMPACT: Triage systems are a key point for the stratification of the priority level of patients and it is therefore evident that they maintain high-quality standards. Through the practice of daily auditing, not only a reduction in the error rate, which ensures patient safety, but also an improvement in triage performance has been demonstrated. NO PATIENT OR PUBLIC CONTRIBUTION: The study did not involve any patients during its conduction.

Can triage nurses accurately interpret the electrocardiogram in the emergency department to predict acute cardiovascular events? A prospective observational study.

AIMS: The prompt recording of the electrocardiogram (ECG) and its correct interpretation is crucial to the management of patients who present to the emergency department (ED) with cardiovascular symptoms. Since triage nurses represent the first healthcare professionals evaluating the patient, improving their ability in interpreting the ECG could have a positive impact on clinical management. This real-world study investigates whether triage nurses can accurately interpret the ECG in patients presenting with cardiovascular symptoms. DESIGN: Prospective, single-centre observational study conducted in a general ED of General Hospital of Merano in Italy. METHODS: For all patients included, the triage nurses and the emergency physicians were asked to independently interpret and classify the ECGs answering to dichotomous questions. We correlated the interpretation of the ECG made by the triage nurses with the occurrence of acute cardiovascular events. The inter-rater agreement in ECG interpretation between physicians and triage nurses was evaluated with Cohen's kappa analysis. RESULTS: Four hundred and ninety-one patients were included. The inter-rater agreement between triage nurses and physicians in classifying an ECG as abnormal was good. Patients who developed an acute cardiovascular event were 10.6% (52/491), and in 84.6% (44/52) of them, the nurse accurately classified the ECG as abnormal, with a sensitivity of 84.6% and a specificity of 43.5%. CONCLUSION: Triage nurses have a moderate ability in identifying alterations in specific components of the ECG but a good ability in identifying patterns indicative of time-dependent conditions correlated with major acute cardiovascular events. IMPACT FOR NURSING: Triage nurses can accurately interpret the ECG in the ED to identify patients at high risk of acute cardiovascular events. REPORTING METHOD: The study was reported according to the STROBE guidelines. NO PATIENT OR PUBLIC CONTRIBUTION: The study did not involve any patients during its conduction.

Analysis of Clinical and Laboratory Risk Factors of Post-Traumatic Intracranial Hemorrhage in Patients on Direct Oral Anticoagulants with Mild Traumatic Brain Injury: An Observational Multicenter Cohort.

BACKGROUND: Assessing the risk of intracranial hemorrhage (ICH) in patients with a mild traumatic brain injury (MTBI) who are taking direct oral anticoagulants (DOACs) is challenging. Currently, extensive use of computed tomography (CT) is routine in the emergency department (ED). OBJECTIVE: This study aims to investigate whether the clinical and laboratory characteristics presented at the ED evaluation can also estimate the risk of post-traumatic ICH in DOAC-treated patients with MTBI. METHODS: A retrospective observational study was conducted in three EDs in Italy from January 1, 2016 to March 15, 2020. All patients treated with DOACs who were evaluated for an MTBI in the ED were enrolled. The primary outcome of the study was the presence of post-traumatic ICH in the head CT performed in the ED. RESULTS: Of 930 patients on DOACs with MTBI who were enrolled, 6.8% (63 of 930) had a post-traumatic ICH and 1.5% (14 of 930) were treated with surgery or died as a result of the ICH. None of the laboratory factors were associated with an increased risk of ICH. On multivariate analysis, previous neurosurgical intervention, major trauma dynamic, post-traumatic loss of consciousness, post-traumatic amnesia, Glasgow Coma Scale score of 14, and evidence of trauma above the clavicles were associated with a higher risk of post-traumatic ICH. The net clinical benefit provided by risk factor assessment appears superior to the strategy of performing CT on all DOAC-treated patients. CONCLUSIONS: Assessment of the clinical characteristics presented at ED admission can help identify DOAC-treated patients with MTBI who are at risk of ICH.

The triage Nurse's ability in electrocardiogram interpretation in real clinical practice.

AIMS AND OBJECTIVE: The study aimed to assess the triage nurse's skill in the recognition of abnormal electrocardiogram during actual clinical practice and to identify nurse- and patient-related factors associated with errors in electrocardiogram interpretation. BACKGROUND: The nurse's ability to interpret the electrocardiogram has only been evaluated in simulation settings and has reported conflicting results. DESIGN: A prospective single-centre observational study. METHODS: During the evaluation of a patient with a cardiovascular symptom, the triage nurses were asked to define whether the 12-lead electrocardiogram performed during the triage evaluation was pathological or non-pathological for the presenting symptom. Patient characteristics and some nurse-related variables were recorded. Inter-rater agreement between the physician and nurse in the electrocardiogram interpretation was considered the primary outcome, while the association of a major acute cardiovascular event related to patient access in the Emergency Department was the secondary outcome. We have followed the STROBE checklist for the preparation of this manuscript. RESULTS: Twenty nurses agreed to participate to the study and collect data. Of the 644 patients enrolled, 21.6% (139/644) reported a pathological electrocardiogram according to the ED Physician. The concordance between nurse and physician was modest with Cohen's Kappa of 0.666. An error in the electrocardiogram interpretation was present in 11% of the patients. Nurses who performed an electrocardiogram course within 1 year and studied electrocardiogram interpretation autonomously presented a lower error rate, while older patients and patients with more previous cardiovascular disease were found to be more associated with an error in electrocardiogram interpretation. CONCLUSIONS: The study demonstrates that triage nurses have a fair ability to interpret the electrocardiogram. RELEVANCE TO CLINICAL PRACTICE: Specific educational programmes for electrocardiogram interpretation could improve the skill of electrocardiogram interpretation by the nurse and enable this instrument to become an indispensable tool in triage assessment.

Multi-centric study for development and validation of a CT head rule for mild traumatic brain injury in direct oral anticoagulants: the HERO-M nomogram.

BACKGROUND: Nomograms are easy-to-handle clinical tools which can help in estimating the risk of adverse outcome in certain population. This multi-center study aims to create and validate a simple and usable clinical prediction nomogram for individual risk of post-traumatic Intracranial Hemorrhage (ICH) after Mild Traumatic Brain Injury (MTBI) in patients treated with Direct Oral Anticoagulants (DOACs). METHODS: From January 1, 2016 to December 31, 2019, all patients on DOACs evaluated for an MTBI in five Italian Emergency Departments were enrolled. A training set to develop the nomogram and a test set for validation were identified. The predictive ability of the nomogram was assessed using AUROC, calibration plot, and decision curve analysis. RESULTS: Of the 1425 patients in DOACs in the study cohort, 934 (65.5%) were included in the training set and 491 (34.5%) in the test set. Overall, the rate of post-traumatic ICH was 6.9% (7.0% training and 6.9% test set). In a multivariate analysis, major trauma dynamic (OR: 2.73, p = 0.016), post-traumatic loss of consciousness (OR: 3.78, p = 0.001), post-traumatic amnesia (OR: 4.15, p < 0.001), GCS < 15 (OR: 3.00, p < 0.001), visible trauma above the clavicles (OR: 3. 44, p < 0.001), a post-traumatic headache (OR: 2.71, p = 0.032), a previous history of neurosurgery (OR: 7.40, p < 0.001), and post-traumatic vomiting (OR: 3.94, p = 0.008) were independent risk factors for ICH. The nomogram demonstrated a good ability to predict the risk of ICH (AUROC: 0.803; CI95% 0.721-0.884), and its clinical application showed a net clinical benefit always superior to performing CT on all patients. CONCLUSION: The Hemorrhage Estimate Risk in Oral anticoagulation for Mild head trauma (HERO-M) nomogram was able to predict post-traumatic ICH and can be easily applied in the Emergency Department (ED).

Machine learning validation through decision tree analysis of the Epidemiology-Based Mortality Score in Status Epilepticus.

OBJECTIVE: This study was undertaken to validate the accuracy of the Epidemiology-Based Mortality Score in Status Epilepticus (EMSE) in predicting the risk of death at 30 days in a large cohort of patients with status epilepticus (SE) using a machine learning system. METHODS: We included consecutive patients with SE admitted from 2013 to 2021 at Modena Academic Hospital. A decision tree analysis was performed using the 30-day mortality as a dependent variable and the EMSE predictors as input variables. We evaluated the accuracy of EMSE in predicting 30-day mortality using the area under the receiver operating characteristic curve (AUC ROC), with 95% confidence interval (CI). We performed a subgroup analysis on nonhypoxic SE. RESULTS: A total of 698 patients with SE were included, with a 30-day mortality of 28.9% (202/698). The mean EMSE value in the entire population was 57.1 (SD = 36.3); it was lower in surviving compared to deceased patients (47.1, SD = 31.7 vs. 81.9, SD = 34.8; p < .001). The EMSE was accurate in predicting 30-day mortality, with an AUC ROC of .782 (95% CI = .747-.816). Etiology was the most relevant predictor, followed by age, electroencephalogram (EEG), and EMSE comorbidity group B. The decision tree analysis using EMSE variables correctly predicted the risk of mortality in 77.9% of cases; the prediction was accurate in 85.7% of surviving and in 58.9% of deceased patients within 30 days after SE. In nonhypoxic SE, the most relevant predictor was age, followed by EEG, and EMSE comorbidity group B; the prediction was correct in 78.9% of all cases (89.6% in survivors and 46.1% in nonsurvivors). SIGNIFICANCE: This validation study using a machine learning analysis shows that the EMSE is a valuable prognostic tool, and appears particularly accurate and effective in identifying patients with 30-day survival, whereas its performance in predicting 30-day mortality is lower and needs to be further improved.

Out-of-hospital versus in-hospital status epilepticus: The role of etiology and comorbidities.

BACKGROUND AND PURPOSE: Our objectives were to identify differences in clinical characteristics between patients with out-of-hospital and in-hospital status epilepticus (SE) onset, and to evaluate the influence of SE onset setting on 30-day mortality and SE cessation. METHODS: We included consecutive patients with SE admitted from 2013-2021 at Modena Academic Hospital. A propensity score was obtained with clinical variables unevenly distributed between the two groups. RESULTS: Seven hundred eleven patients were included; 55.8% (397/711) with out-of-hospital and 44.2% (314/711) with in-hospital onset. Patients with in-hospital SE onset were older and had a higher frequency of comorbidities, acute and/or potentially fatal etiologies, impaired consciousness before treatment, and nonconvulsive or myoclonic SE. No difference was found in SE cessation between the groups. Patients with in-hospital SE had higher 30-day mortality (127/314, 62.9% vs. 75/397, 37.1%; p < 0.001). In-hospital onset was an independent risk factor for 30-day mortality (adjusted odds ratio = 1.720; 95% confidence interval = 1.107-2.674; p = 0.016). In the propensity group (n = 244), no difference was found in 30-day mortality and SE cessation between out-of-hospital and in-hospital SE onset groups (36/122, 29.5% vs. 34/122, 27.9%; p = 0.888; and 47/122, 38.5% vs. 39/122; 32%; p = 0.347, respectively). CONCLUSIONS: In-hospital SE is associated with higher 30-day mortality without difference in SE cessation. The two groups differ considerably for age, acute and possibly fatal etiologies, comorbidities, and SE semiology. The patient location at SE onset is an important prognostic predictor. However, the increased mortality is probably unrelated to the setting of SE onset and reflects intrinsic prognostic predictors.

Rapid antigen test to identify COVID-19 infected patients with and without symptoms admitted to the Emergency Department.

PURPOSE: Early detection of SARS-CoV-2 patients is essential to contain the pandemic and keep the hospital secure. The rapid antigen test seems to be a quick and easy diagnostic test to identify patients infected with SARS-CoV-2. To assess the possible role of the antigen test in the Emergency Department (ED) assessment of potential SARS-CoV-2 infection in both symptomatic and asymptomatic patients. METHODS: Between 1 July 2020 and 10 December 2020, all patients consecutively assessed in the ED for suspected COVID-19 symptoms or who required hospitalisation for a condition not associated with COVID-19 were subjected to a rapid antigen test and RT-PCR swab. The diagnostic accuracy of the antigen test was determined in comparison to the SARS-CoV-2 PCR test using contingency tables. The possible clinical benefit of the antigen test was globally evaluated through decision curve analysis (DCA). RESULTS: A total of 3899 patients were subjected to antigen tests and PCR swabs. The sensitivity, specificity and accuracy of the antigen test were 82.9%, 99.1% and 97.4% (Cohen's K = 0.854, 95% CI 0.826-0.882, p < 0.001), respectively. In symptomatic patients, sensitivity was found to be 89.8%, while in asymptomatic patients, sensitivity was 63.1%. DCA appears to confirm a net clinical benefit for the preliminary use of antigen tests. CONCLUSIONS: The antigen test performed in the ED, though not ideal, can improve the overall identification of infected patients. While it appears to perform well in symptomatic patients, in asymptomatic patients, although it improves their management, it seems not to be definitive.

Risk of delayed intracranial haemorrhage after an initial negative CT in patients on DOACs with mild traumatic brain injury.

BACKGROUND: Repeat head CT in patients on direct oral anticoagulant therapy (DOACs) with minor traumatic brain injury (MTBI) after an initial CT scan without injury on arrival in the Emergency Department (ED) is a common clinical practice but is not based on clear evidence. AIM: To assess the incidence of delayed intracranial haemorrhage (ICH) in patients taking DOACs after an initial negative CT and the association of clinical and risk factors presented on patient arrival in the ED. METHODS: This retrospective multicentre observational study considered patients taking DOACs undergoing repeat CT after a first CT free of injury for the exclusion of delayed ICH after MTBI. Timing between trauma and first CT in the ED and pre- or post-trauma risk factors were analysed to assess a possible association with the risk of delayed ICH. RESULTS: A total of 1426 patients taking DOACs were evaluated in the ED for an MTBI. Of these, 68.3% (916/1426) underwent a repeat CT after an initial negative CT and 24 h of observation, with a rate of delayed ICH of 1.5% (14/916). Risk factors associated with the presence of a delayed ICH were post-traumatic loss of consciousness, post-traumatic amnesia and the presence of a risk factor when the patient presented to the ED within 8 h of the trauma. None of the patients with delayed ICH at 24-h repeat CT required neurosurgery or died within 30 days. CONCLUSIONS: Delayed ICH is an uncommon event at the 24-h control CT and does not affect patient outcome. Studying the timing and characteristics of the trauma may indicate patients who may benefit from more in-depth management.

Development and Validation of a Nomogram Based on the Epidemiology-Based Mortality Score in Status Epilepticus (EMSE) Parameters to Predict 30-day Mortality in Status Epilepticus.

BACKGROUND: To develop a nomogram using the parameters of the Epidemiology-Based Mortality Score in Status Epilepticus (EMSE) and to evaluate its accuracy compared with the EMSE alone in the prediction of 30-day mortality in patients with status epilepticus (SE). METHODS: We included a cohort of patients with SE aged ≥ 21 years admitted from 2013 to 2021. Regression coefficients from the multivariable logistic regression model were used to generate a nomogram predicting the risk of 30-day mortality. Discrimination of the nomogram was evaluated using the area under the receiver operating characteristic curve (AUCROC) with 95% confidence interval. Internal validation was performed by bootstrap resampling. RESULTS: Among 698 patients with SE, the 30-day mortality rate was 28.9% (202 of 698). On the multivariable analysis, all EMSE parameters (except for the comorbidity group including metastatic solid tumor or AIDS) were associated with a significantly higher risk of 30-day mortality and were included in the nomogram. The discriminatory capability of the nomogram with bootstrap resampling (5000 resamples) had an AUCROC of 0.830 (95% confidence interval 0.798-0.862). Conversely, the AUCROC of the EMSE was 0.777 (95% confidence interval 0.742-0.813). Thus, the probability that a patient who died within 30 days from SE had a higher score than a patient who survived was 83%, indicating good discriminatory power of the nomogram. Conversely, the risk predicted using the EMSE alone was 77%. The nomogram was well calibrated. CONCLUSIONS: A nomogram based on EMSE parameters appears superior to the EMSE in predicting the risk of 30-day mortality after SE. The discrimination and calibration of the nomogram shows a better predictive accuracy than the EMSE alone.

Triage assessment of transitory loss of consciousness in the emergency department-A retrospective observational study.

AIM: To establish how the Manchester Triage System can correctly prioritize patients admitted to the emergency department for transitory loss of consciousness in relation to their risk of presenting severe acute disease. DESIGN: A observational retrospective study. METHODS: A total of 2291 patients who required a triage evaluation for a transitory loss of consciousness at the emergency department of Merano Hospital between 1 January 2017 and 30 June 2019 were considered. Transitory loss of consciousness was classified according to European Society of Cardiology guidelines. The baseline characteristics of the patients were collected and divided according to the priority level assigned at triage into two different study groups: high priority (red/orange) and low priority (blue/green/yellow). The composite outcome of the study was defined as the diagnosis of a severe acute disease. RESULTS: Of the patients enrolled, 17% (390/2291) had a high-priority code and 83% (1901/2291) received a low-priority code. Overall, a severe acute disease was present in 16.9% of patients (387/2291). The Manchester Triage System had a sensitivity of 42.4%, a specificity of 88.1% and an accuracy of 80.4% for predicting severe acute disease. The discriminatory ability had an area under the receiver operating characteristic curve of 0.651 (CI 95%: 0.618-0.685). CONCLUSIONS: Despite the good specificity, the low sensitivity does not currently allow the Manchester Triage System to completely exclude patients with a severe acute disease who presented in the emergency department for a transitory loss of consciousness. Therefore, it is important to develop precise nursing tools or assessments that can improve triage performance. IMPACT: The assessment of a complex symptom can create difficulties in the stratification of patients in triage, assigning low-priority codes to patients with a severe disease. Additional tools are needed to allow the correct triage assessment of patients presenting with transitory loss of consciousness.

Severity of SARS-CoV-2 infection and albumin levels recorded at the first emergency department evaluation: a multicentre retrospective observational study.

BACKGROUND: The aim of this study was to investigate the association between serum albumin levels in the ED and the severity of SARS-CoV-2 infection. METHODS: This is a retrospective observational study conducted from 15 March 2020 to 5 April 2020 at the EDs of three different hospitals in Italy. Data from 296 patients suffering from COVID-19 consecutively evaluated at EDs at which serum albumin levels were routinely measured on patients' arrival in the ED were analysed. Albumin levels were measured, and whether these levels were associated with the presence of severe SARS-CoV-2 infection or 30-day survival was determined. Generalised estimating equation models were used to assess the relationship between albumin and study outcomes, and restricted cubic spline (RCS) regression was used to plot the adjusted dose-effect relationship for possible clinical confounding factors. RESULTS: The mean albumin level recorded on entry was lower in patients with severe SARS-CoV-2 infection than in those whose infections were not severe (3.5 g/dL (SD 0.3) vs 4 g/dL (SD 0.5)) and in patients who had died at 30 days post-ED arrival compared with those who were alive at this time point (3.3 g/dL (SD 0.3) vs 3.8 g/dL (SD 0.4)). Albumin <3.5 g/dL was an independent risk factor for both severe infection and death at 30 days, with adjusted odd ratios of 2.924 (1.509-5.664) and 2.615 (1.131-6.051), respectively. RCS analysis indicated that there was an adjusted dose-response association between the albumin values recorded on ED and the risk of severe infection and death. CONCLUSION: Albumin levels measured on presentation to the ED may identify patients with SARS-CoV-2 infection in whom inflammatory processes are occurring and serve as a potentially useful marker of disease severity and prognosis.